L2 P4 · original

compile_fail 4_Conv2d_Mish_Mish.py

model

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original

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started

2026-05-05T00:29:56Z

compile error: Error building extension 'conv_mish2_fused_ext': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv_mish2_fused_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_3a1bb9070a3f/conv

timingkernelbench.timing

kernel runtime

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kernel mean / median / std

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reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a convolution, applies Mish, and another Mish.
    """
    def __init__(self, in_channels, out_channels, kernel_size):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)

    def forward(self, x):
        x = self.conv(x)
        x = torch.nn.functional.mish(x)
        x = torch.nn.functional.mish(x)
        return x

batch_size   = 64  
in_channels  = 64  
out_channels = 128  
height = width = 256
kernel_size = 3

def get_inputs():
    return [torch.rand(batch_size, in_channels, height, width)]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size]

turn 0 · FINAL · 1 tool call(s) · 40.9s turn · 40.9s LLM · 9,803 chars (click to expand)

turn 040.9s turn · 40.9s LLM · 0.0s tools · FINAL

reasoning

**Considering computational efficiency** I’m weighing the memory and compute demands of the operations, like on the tensor with dimensions of 64*128*254*254, resulting in about 528 million floats, which is roughly 2.1GB. The Mish activation function reads and writes several gigabytes with softplus and tanh, making it significant. While I could potentially combine two Mish functions into one read/write cycle, I still compute exp and tanh twice. There could be speedup opportunities here, so I need to explore compiling a C++ extension. **Evaluating extension calls** I’m considering whether it might be better to use extension wrapper calls at `::conv2d` and kernel fused, instead of relying on Python’s `F.conv`. It seems like compiling the forward calls in the extension could improve performance. Overall, I want to ensure that I’m making the most efficient choices for the operations I’m working with. It’s important for me to think this through carefully!

call → submit_kernel

callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n\n_cpp_src = r'''\n#include <torch/extension.h>\n\nvoid mish2_inplace_cuda(torch::Tensor y);\n\ntorch::Tensor conv_mish2_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n // Use the same highly optimized convolution backend as torch conv2d, then\n // fuse the two Mish activations into one CUDA pass over the convolution output.\n auto y = torch::conv2d(x, weight, bias, {1, 1}, {0, 0}, {1, 1}, 1);\n mish2_inplace_cuda(y);\n return y;\n}\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float softplus_f(float x) {\n // Stable softplus; matches PyTorch Mish closely for fp32 while avoiding\n // unnecessary exp/log work in the saturated tails.\n if (x > 20.0f) return x;\n if (x < -20.0f) return expf(x);\n return log1pf(expf(x));\n}\n\n__device__ __forceinline__ float mish_f(float x) {\n return x * tanhf(softplus_f(x));\n}\n\n__global__ void mish2_kernel(float* __restrict__ data, int64_t n) {\n int64_t tid = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n for (int64_t i = tid; i < n; i += stride) {\n float v = data[i];\n v = mish_f(v);\n v = mish_f(v);\n data[i] = v;\n }\n}\n\nvoid mish2_inplace_cuda(torch::Tensor y) {\n const int threads = 256;\n int64_t n = y.numel();\n if (n == 0) return;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);\n}\n'''\n\n_ext = load_inline(\n name='conv_mish2_fused_ext',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['conv_mish2_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n with_cuda=True,\n verbose=False,\n)\n\n\nclass ModelNew(nn.Module):\n \"\"\"\n Same module interface as the reference. Forward is dispatched to a small\n C++/CUDA extension which performs conv2d and a fused double-Mish epilogue.\n \"\"\"\n def __init__(self, in_channels, out_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n\n def forward(self, x):\n return _ext.conv_mish2_forward(x, self.conv.weight, self.conv.bias)\n" }

result ← submit_kernel (fail)

failsubmit_kernel submit_kernel FAILED: kernel did not compile. Error building extension 'conv_mish2_fused_ext': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv_mish2_fused_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_3a1bb9070a3f/conv_mish2_fused_ext/main.cpp -o main.o FAILED: [code=1] main.o /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv_mish2_fused_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_3a1bb9070a3f/conv_mish2_fused_ext/main.cpp -o main.o /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_3a1bb9070a3f/conv_mish2_fused_ext/main.cpp: In function 'at::Tensor conv_mish2_forward(at::Tensor, at::Tensor, at::Tensor)': /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_3a1bb9070a3f/conv_mish2_fused_ext/main.cpp:10:27: error: call of overloaded 'conv2d(at::Tensor&, at::Tensor&, at::Tensor&, <brace-enclosed initializer list>, <brace-enclosed initializer list>, <brace-enclosed initializer list>, int)' is ambiguous 10 | auto y = torch::conv2d(x, weight, bias, {1, 1}, {0, 0}, {1, 1}, 1); | ~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In file included from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/Functions.h:607, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ExpandUtils.h:5, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/input_metadata.h:4, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/function.h:8, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/custom_function.h:8, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:6, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/extension.h:6, from /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_3a1bb9070a3f/conv_mish2_fused_ext/main.cpp:1: /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/conv2d.h:28:19: note: candidate: 'at::Tensor at::conv2d(const Tensor&, const Tensor&, const std::optional<Tensor>&, IntArrayRef, IntArrayRef, IntArrayRef, int64_t)' 28 | inline at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) { | ^~~~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/conv2d.h:50:19: note: candidate: 'at::Tensor at::conv2d(const Tensor&, const Tensor&, const std::optional<Tensor>&, IntArrayRef, c10::string_view, IntArrayRef, int64_t)' 50 | inline at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) { | ^~~~~~ [2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=conv_mish2_fused_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l123_single_gpt/original/gpt-5.5-priority/level_2_problem_4_cache/k_3a1bb9070a3f/conv_mish2_fused_ext/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used ninja: build stopped: subcommand failed.

final submitted kernel (82 lines, 2,459 chars) — full source, untruncated

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline


_cpp_src = r'''
#include <torch/extension.h>

void mish2_inplace_cuda(torch::Tensor y);

torch::Tensor conv_mish2_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {
    // Use the same highly optimized convolution backend as torch conv2d, then
    // fuse the two Mish activations into one CUDA pass over the convolution output.
    auto y = torch::conv2d(x, weight, bias, {1, 1}, {0, 0}, {1, 1}, 1);
    mish2_inplace_cuda(y);
    return y;
}
'''

_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>

__device__ __forceinline__ float softplus_f(float x) {
    // Stable softplus; matches PyTorch Mish closely for fp32 while avoiding
    // unnecessary exp/log work in the saturated tails.
    if (x > 20.0f) return x;
    if (x < -20.0f) return expf(x);
    return log1pf(expf(x));
}

__device__ __forceinline__ float mish_f(float x) {
    return x * tanhf(softplus_f(x));
}

__global__ void mish2_kernel(float* __restrict__ data, int64_t n) {
    int64_t tid = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;
    int64_t stride = (int64_t)blockDim.x * gridDim.x;
    for (int64_t i = tid; i < n; i += stride) {
        float v = data[i];
        v = mish_f(v);
        v = mish_f(v);
        data[i] = v;
    }
}

void mish2_inplace_cuda(torch::Tensor y) {
    const int threads = 256;
    int64_t n = y.numel();
    if (n == 0) return;
    int blocks = (int)((n + threads - 1) / threads);
    if (blocks > 65535) blocks = 65535;
    mish2_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);
}
'''

_ext = load_inline(
    name='conv_mish2_fused_ext',
    cpp_sources=_cpp_src,
    cuda_sources=_cuda_src,
    functions=['conv_mish2_forward'],
    extra_cflags=['-O3'],
    extra_cuda_cflags=['-O3', '--use_fast_math'],
    with_cuda=True,
    verbose=False,
)


class ModelNew(nn.Module):
    """
    Same module interface as the reference.  Forward is dispatched to a small
    C++/CUDA extension which performs conv2d and a fused double-Mish epilogue.
    """
    def __init__(self, in_channels, out_channels, kernel_size):
        super(ModelNew, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)

    def forward(self, x):
        return _ext.conv_mish2_forward(x, self.conv.weight, self.conv.bias)